Forecasting for Strategic Impact: A Two-Part Series-R Studio Handout

R Studio Handout

Pdf Summary

This document provides a step-by-step guide for implementing an ARIMA (AutoRegressive Integrated Moving Average) model for time series forecasting using R and RStudio. First, it instructs users to install and load two essential R libraries: "forecast" and "tseries." Users are then guided to set their working directory in R to access relevant data files.<br /><br />The dataset is imported via a file browser with the command `read.csv(file.choose())`. The guide then explains how to convert a specific variable—in this case, the first 314 observations of the 'FRP' column—into a time series object, specifying a monthly frequency starting from January 2005.<br /><br />To fit the ARIMA model, the `auto.arima()` function is used, which automatically selects the best-fitting ARIMA model and accounts for seasonality by setting `seasonal = TRUE`. After fitting the model, the document shows how to generate forecasts for the next nine months with a 50% confidence interval.<br /><br />Visualization of the forecast, including historical and predicted values, is done using the `plot()` function. Finally, the forecast results can be saved to a CSV file in the working directory for further use or analysis.<br /><br />Overall, this guide provides clear R code snippets to carry out time series forecasting with seasonal ARIMA models, from data import and transformation through model fitting, forecasting, visualization, and result export—making it a practical resource for analysts working with monthly time series data.

Keywords

ARIMA model

time series forecasting

R programming

RStudio

forecast package

tseries package

auto.arima()

seasonal ARIMA

time series data

data visualization